Systems and methods for improving soft tissue contrast, multiscale modeling and spectral ct

ABSTRACT

Systems and methods for improving soft tissue contrast, characterizing tissue, classifying phenotype, stratifying risk, and performing multi-scale modeling aided by multiple energy or contrast excitation and evaluation are provided. The systems and methods can include single and multi-phase acquisitions and broad and local spectrum imaging to assess atherosclerotic plaque tissues in the vessel wall and perivascular space.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. provisionalpatent application No. 63/147,609, filed on Feb. 9, 2021, the entirecontents of which are incorporated herein by reference in its entiretyand owned by the assignee of the instant application.

GOVERNMENT RIGHTS

This work was supported in part by the National Heart, Lung, and BloodInstitute, contract number HL 126224. The government may have certainrights in the invention.

FIELD OF THE INVENTION

The invention relates to computer-aided phenotyping (CAP) of diseasewhich can include applying computerized image analysis and/or datafusion algorithms to patient data. In particular, the invention relatesto quantitative imaging and analytics for elucidating the diseaseprocess of atherosclerosis, including techniques for improving softtissue contrast, multi-scale modeling and/or spectral CT.

BACKGROUND OF THE INVENTION

Atherosclerosis can be life threatening, particularly in agingpopulations, but even among the relatively young. Current methods fordiagnosing atherosclerosis, for example, the use of blood markers e.g.,cholesterol levels) and/or determining the degree to which the lumen isnarrowed (stenosis) are limited, and thus can result in suboptimaltreatment decisions (e.g., to perform or not to perform surgeries, orprescribe intensive medical therapy). For example, many vascularsurgeries do not benefit the patient, some that need surgeries don't getthem, and many could be effectively treated with drugs but may not beprescribed them.

Current tools can analyze a blood vessel lumen, but this can beinsufficient for truly diagnosing atherosclerosis, as atherosclerosis isa disease of the vessel wall, rather than the blood or the channelthrough which it flows. High rates of misclassified risk level,inability to assess likely response to drug therapy, and/or inability tomeasure response to drugs can occur.

Currently, radiological imagining can be used as a non-invasive and safemethod for locating disease origin. Current medical imagining tools caninclude computed tomography (CT, including single energy, multi-energy,or spectral CT), magnetic resonance imaging (MR, MRA, DCE-MRI, ormulti-contrast MRI), ultrasound (b-mode or intravascular US), andtargeted contrast agent approaches with various imaging modalities.

Enhanced imaging techniques have made medical imaging an essentialcomponent of patient care. Imaging can be valuable because it canprovide spatially and temporally localized anatomic and/or functionalinformation, using non- or minimally invasive methods. However,techniques to deal with increasing resolution can be desired, both toexploit patterns and/or signatures in the data typically not readilyassessed with the human eye, as well as to, for example, manage a largemagnitude of data to efficiently integrate it into the clinicalworkflow. With newer high-resolution imaging techniques, unaided, theradiologist can “drown” in data. Therefore, in order to, for example,integrate quantitative imaging for individual patient management it canbe desirable to provide a class of decision support informatics tools toenable further exploiting the capabilities of imaging within therealities of existing tool work flows and/or reimbursement constraints.

Currently, imaging of atherosclerosis is routinely performed bothinvasively through catheterization as well as non-invasively byultrasound, CT, MR, and using nuclear medicine techniques. The mosttypical assessment is luminal stenosis. Recent progress that has beenmade has been in the determination of fractional flow reserve.

One difficulty with current imaging of atherosclerosis can include lackof robustness in the method used. For example, current methods typicallyonly provide a low level of contrast between blood vessel outer wall andperivascular tissues, thus making it difficult to distinguish betweenthe two. Some current methods simply employ annular rings around a lumenwithout specific determination of outer wall boundary. Vessel tapering,branching vessels, nearby tissues, etc. can also be problematic.

Another difficulty with current imaging of atherosclerosis can be due toa particular imaging device interrogating tissue using a limitedexcitation, and that despite the utility of multi-contrast MR on the onehand, or multi-energy CT on the other, the result can be a degree ofnon-specific response in the produced signal.

SUMMARY OF THE INVENTION

Advantages of the invention can include improved soft tissue contrast.Some advantages of the invention can include improved characterizingtissue. Some advantages of the invention can include classifyingphenotype. Some advantages of the invention can include stratifyingrisk. Some advantages of the invention can include performingmulti-scale modeling aided by multiple energy or contrast excitation andevaluation.

Some advantages of the invention can include application of single vs.multi-phase acquisitions as well as broad spectrum spectral CT to assessatherosclerotic plaque tissues in the vessel wall and perivascularspace. Some advantage of the invention can include distributed tissuetypes, as overlays on focally organized tissues.

In one aspect, the invention involves a computerized method forimproving soft tissue analysis. The method can also involve obtaining aplurality of radiological images of patient, where each of theradiological images is obtained using different excitations. The methodcan also involve selecting a process among a plurality of processes toanalyze the plurality of excitations based on an expected soft tissuetype. The method can also involve segmenting the processed plurality ofexcitations to display the soft tissue.

In some embodiments, the plurality of radiological image arecomputerized tomography (CT) images and the different excitations aredifferent x-ray energy. In some embodiments, the plurality ofradiological image are Magnetic Resonance (MR) images and the differentexcitations are different radio frequency pulses.

In some embodiments, the plurality of radiological image are ultrasoundimages and the different excitations are different frequencies.

In some embodiments, the invention involves determining, via, thecomputing device, a first tissue type in a region of interest based onthe plurality of radiological images of the patient, wherein the firsttissue type is a represented by a grid of points across the region ofinterest, and determining, via the computing device, a second tissuetype, in the region interest based on the plurality of radiologicalimages of the patient, wherein the second tissue type is a focal regionin the region of interest, wherein at least some of the grid points ofthe first tissue type coincide in position with the second tissue type.

In some embodiments, the plurality of processes comprises a digitalsubtraction process, digital addition process, a multivariatestatistical process, or an excitation selection process. In someembodiments, the digital subtraction process comprises subtracting afirst subset of the plurality of radiological images from one or more ofthe plurality of radiological images not in the subset. the digitaladdition process comprises averaging the received plurality ofradiological images.

In some embodiments, the multivariate statistical process comprisescombining the plurality of radiological images and removing inter-classdependencies through a multi-variate statistical approach.

In some embodiments, the plurality of radiological images are CT imagesand each of the plurality of CT images are formed by directing, via afirst x-ray source, a first x-ray attenuation to an energy integratingdetector, wherein the energy integrated detector is dimensioned toproduce an image of a predetermined area of the patient, directing, viaa second x-ray source, a second x-ray attenuation to a photon countingdetector, where the photon counting detector to produce an image of aspecific tissue target within the predetermined area; and producing, viaa processor, a final CT image based on the image of the predeterminedarea of the patient and the image of the specific tissue target withinthe predetermined image.

In some embodiments, the excitation selection process involves selectinga particular radiological image of the plurality of radiological imagesbased on the tissue type.

In another aspect, the invention includes a hybrid computerizedtomography (CT) scanner. The hybrid CT scanner can include a first x-raysource that directs a first x-ray attenuation to an energy integratingdetector, wherein the energy integrated detector is dimensioned toproduce an image of a predetermined area of a patient. The hybrid CTscanner can also include a second x-ray source that directs a secondx-ray attenuation to a photon counting detector, where the photoncounting detector to produce an image of a specific tissue target withinthe predetermined area. The hybrid CT scanner can also include aprocessor to produce a final CT image based on the image of thepredetermined area of the patient and the image of the specific tissuetarget within the predetermined image.

In some embodiments, the energy integrating detector and the photoncounting detector are positioned to interrogate the same field of view.In some embodiments, the image of a predetermine area of a patient is agrayscale CT image. In some embodiments, the image of the specifictissue target within the predetermined image is a spectral CT image. Insome embodiments, the energy integrating detector relative to its firstx-ray source is positioned at a 90 degree difference between the photoncounting detector and its second x-ray source.

In some embodiments, the processor is configured to analyze tissuetypes, the analysis comprising selecting a process among a plurality ofprocesses to analyze the final CT image based on an expected soft tissuetype, and segmenting the processed final CT image to display the softtissue.

In some embodiments, the photon counting detector comprises multipleenergy bins that are configured to image a specific tissue target.

In another aspect, the invention involves a computerized method fordetermining and displaying mixed tissue types. The method can involvereceiving, via a computing device, a radiological image of a patient.The method can involve determining, via the computing device, a firsttissue type in a region of interest based on the radiological image ofthe patient, wherein the first tissue type is a represented by a grid ofpoints across the region of interest. The method can involvedetermining, via the computing device, a second tissue type, in theregion interest based on the radiological image of the patient, whereinthe second tissue type is a focal region in the region of interest,wherein at least some of the grid points of the first tissue typecoincide in position with the second tissue type.

In some embodiments, the plurality of radiological image arecomputerized tomography (CT) images, Magnetic Resonance (MR) images, orultrasound images. In some embodiments, the first tissue type and thesecond tissue type are overlaid when displayed. In some embodiments, thegrid of points has varying densities.

In some embodiments, the first tissue type is micro calcification, andthe second tissue type is LNRC, dense calcification, or IPH.

In some embodiments, the method involves performing, via the computingdevice, multiple energy photon-counting K-edge subtraction imaging onthe CT images, performing, via the computing device, spectral imagedenoising with regularization models on the CT images, improving thesignal to noise ratio, via the computing device, of the calcium on thek-edge subtracted and de-noised CT images, and segmenting, via thecomputing device, the improved calcium images to represent one or bothof focally dense calcification and distributed microcalcification.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting examples of embodiments of the disclosure are describedbelow with reference to figures attached hereto that are listedfollowing this paragraph. Dimensions of features shown in the figuresare chosen for convenience and clarity of presentation and are notnecessarily shown to scale.

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features and advantages thereof, can beunderstood by reference to the following detailed description when readwith the accompanied drawings. Embodiments of the invention areillustrated by way of example and not limitation in the figures of theaccompanying drawings, in which like reference numerals indicatecorresponding, analogous or similar elements, and in which:

FIG. 1 is a diagram of an example radiological input and processedoutputs, according to some embodiments of the invention.

FIG. 2 is a diagram showing an example of atherosclerosis progressesaccording to common drivers across arterial beds in differing degreesbased on evolutionary differences and local factors, according to someembodiments of the invention.

FIG. 3 is an example of a first set of CT scan and a second set of CTscan that shows an improved soft tissue contrast, according to someembodiments of the invention.

FIG. 4 is a diagram of a system for improving soft tissue segmentation,according to some embodiments of the invention.

FIG. 5 is an example of a method for improving soft tissue segmentation,according to some embodiments of the invention.

FIG. 6 is a cross-sectional front view of a hybrid computed tomography(CT) scanner, according to some embodiments of the invention.

FIG. 7A shows an example of multi-scale association linking radiologyscale plaque morphology to molecular determinants, according to someembodiments of the invention.

FIG. 7B is an example of multi-scale modeling, according to someembodiments of the invention.

FIG. 7C is an example of expanded multi-scale modeling, according tosome embodiments of the invention.

FIG. 8 is an example output screen showing multiple simulated event-freesurvival possibilities under untreated and under various treatmentscenarios, according to some embodiments of the invention.

FIG. 9 shows a method for determining and displaying mixed tissue types,according to some embodiments of the invention.

FIG. 10 shows a method for determining and displaying mixed tissue typesof a microcalcification screen and dense calcification regions usingradiological images of multiple energy spectral CT images, according tosome embodiments of the invention.

FIG. 11 are diagrams showing examples of the method of FIG. 10,according to some embodiments of the invention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn accuratelyor to scale. For example, the dimensions of some of the elements can beexaggerated relative to other elements for clarity, or several physicalcomponents can be included in one functional block or element.

DETAILED DESCRIPTION

In general, the invention involves systems and methods for soft tissuecontrast, characterizing tissue, classifying phenotype, stratifyingrisk, and/or performing multi-scale modeling aided by multiple energy orcontrast excitation and evaluation. The invention can involveapplication of single vs. multi-phase image acquisitions and can includebroad spectrum spectral CT to, for example, assess atheroscleroticplaque tissues in a vessel wall and/or perivascular space.

In general, the invention can involve exploiting differing responses bytissue to multi-energy or spectral image sets using a software approach.The invention can also include a hardware configuration that can furtherdiscriminate tissues in clinically relevant ranges. The spectral imagesobtained via the multi-energy levels and/or a broad spectrum can formthe input for one or more algorithms that can improve tissuesegmentation.

In some embodiments, the algorithm can include a exploiting a non-linearresponse to differing tissues by noting that noise can be similar acrossmultiple energies, but that the tissue can be different and applying anaveraging technique that can result in a higher signal to noise ratio.

In some embodiments, differing tissues can resolve better in one energylevel versus another. In some embodiments, different energies areselected for different tissues. In some embodiments, a non-linearresponse to differing tissues is exploited using a digital subtractionapproach. In some embodiments, differing tissue responses are combinedfrom each of optimal energy levels and inter class dependencies areremoved through a multi-variate statistical approach. In variousembodiments, each of these foregoing embodiments are applied to hardwareconfigurations that amplify such differences, whether designed based onmaterial composition or other physical phenomenon, to achieve benefit.Examples include tissues differentiated by molecular properties,cellular and molecular milieu, material density distributions,morphological presentation, response to stimuli, etc.

In some embodiments, distributed tissue types are used as overlays onfocally organized tissues.

FIG. 1 is a diagram 100 of an example radiological input 110 andprocessed outputs, according to some embodiments of the invention. Theprocessed outputs can include an image 120 that identifiesthree-dimensional regions for various measurands of calcification, LipidRich Necrotic Core (LRNC) and matrix, and an image 130 that presentstables and graphs of data related to the various measurands. As isapparent to one of ordinary skill in the art, the measurands shown inFIG. 1 are examples and other measurands can be included.

FIG. 2 is a diagram 200 showing an example of atherosclerosis progressesaccording to common drivers across arterial beds in differing degreesbased on evolutionary differences and local factors, according to someembodiments of the invention.

A characterization of coronary artery disease can be accomplished by anassessment of two related but distinct mechanisms: an ability of thearterial system to meet demand under stress, stress-induced ischemia(measured, for example, as FFR or iFR), which can require the deliveryof more oxygenated blood to downstream perfused tissues; and apropensity of plaque to physically disrupt or embolize, Infarction Risk(HRP). Therapeutic pathways can be selected optimally based on processedoutputs (e.g., processed outputs as described above in FIG. 1 andfurther below) to characterize a degree to which each of the distinctmechanisms are present, for example, whether one but not the other,neither, or both, at various levels of magnitude. In some embodiment, alikelihood of the conditions, low vs. high, is used to predict a timeuntil an adverse event may take place at the individual patient level. Arecommendation for based on an FRR/HRP ratio can be made. Therecommendation can be based on the likelihood of the conditions and caninclude provide maintenance medications, intensive medical therapy,treat symptoms, or revascularization.

In some scenarios, it can be desirable to identify tissue types withcomplex cellular or molecular level milieu (e.g., complex tissues) thatmanifests as overlapping material densities. Current methods can beerroneous and fail to correctly identify the tissue types. Currentmethod can include simple thresholding of material density as given byvoxel Hounsfield units (HUs). For example, LRNC or idiopathic pulmonaryhemosiderosis (IPH) may not easily and/or reliably identified, and/orbiological processes that produce them and/or that are triggered inresponse to them can be complex. LRNC can be variously composed of lipiddeposits, cholesterol crystals, apoptotic cellular debris, macrophagesand/or calcifications. IPH can be variously composed of intact and/orruptured red blood cells, macrophages, hemorrhagic debris, fibrin,cholesterol crystals, and/or calcifications. The complex tissues canpresent difficulty for systems using a single excitation energy for CT,or characteristics RF profile for MR, heterogeneity of plaque componentscan be exploited based on differences in the tissues response to theexcitation, e.g., how they respond to one energy vs. another, or oneradio frequency vs. another, which can respond differently to variousenergies. Therefore, it can be desirable to relax a strict dependency onmaterial density. In some embodiments, different density distributionscan be mathematically fit to exemplars from histopathology to relax astrict dependency on material density.

In some embodiments, applying multiple energy signals during the CT scanand using digital subtraction, averaging, and/or selection techniques toamplify the signal for one tissue type over another, and all over thelevel of noise can be used to relax a strict dependence on materialdensity. In some embodiments, the HU of adjacent voxels may be analyzedas a distribution rather than only as individual voxels, by maximizingthe Mumford-Shah functional, as an exemplary embodiment.

Analogously to the stains which are applied to tissues which assistpathologists exercise judgement when they outline LRNC and IPH, thepresent invention can be understood as applying “digital stains” to theimagery to accomplish a similar amplification because they areheterogeneous and overlapping tissues are composed of multiple celltypes, with differing densities, each responding differently todiffering incident energy. Pathology annotation takes this into accountas pathologists mark tissue boundaries of LRNC and IPH on CTA need to beestablished using similar judgment to what pathologists do on histologyso that clinical insights based on it apply. This requires more thanjust applying HU thresholds. It requires a mathematical formalism thatcan be used to mimic the judgment used by human pathologists trained inrecognizing tissue.

MRI, ultrasound, nuclear medicine, and/or other imaging modalities canbe reconstructed with various strengths and weakness prior topost-processing. FIG. 3 is an example of a first set of CT scans 310 a,320 a and a second set of CT scans 310 b, 320 b that shows an improvedsoft tissue contrast, according to some embodiments of the invention.One CT scan 310 a in the first set of CT scans shows two regions 330 aand 340 a that are improved in a CT scan 310 b of the second set of CTscans as seen in 330 b, 340 b. One CT scan 320 a is the first set of CTscans shows one region 350 a that is improved in a CT scan 320 b of thesecond set of CT scans as seen in 350 b.

FIG. 4 is a diagram of a system 400 for improving soft tissuesegmentation, according to some embodiments of the invention. The systemcan include an imaging device 410, a classification unit 430.

The imaging device 410 can be a CT scanner, Magnetic Resonance Imaging,Ultrasound, and/or other imaging devices as are known in the art. Theimaging device 410 can obtain one or a plurality of radiological images(e.g., CT, MR, Ultrasound) 420. The plurality of radiological images 420can be monochromatic. The plurality of monochromatic images 420 can betaken at a plurality of excitations. As is apparent to one of ordinaryskill in the art, a plurality of images taken at a plurality ofexcitations can be as a result of transmission, a reception, or anycombination thereof.

The plurality of excitations can include a plurality of energies, radiofrequencies pulses (e.g., sequences), and/or differing frequencies ortimings, as used by CT, MRI, and/or ultrasound, respectively. In someembodiments, each of the plurality of monochromatic images 420 are takenat a unique excitation (e.g., unique x-ray energy, whether astransmitted, as received, or both, and as a narrow energy range, or as abroader range) of a CT scanner, unique pulse sequences for an MRI,unique frequencies and/or timings for ultrasound).

A number of the plurality of monochromatic images 420 can depend on whattype of tissues or phenotypes that are desired to be discriminated.

In some embodiments, some of the plurality of monochromatic images 420are taken at the same excitations and others of the plurality ofmonochromatic images 420 are taken at different excitations. Forexample, each time two monochromatic images 420 are taken, theexcitation can change. In another example, just the first twomonochromatic images 420 are the same excitation and the remainingmonochromatic images 420 are taken at a unique excitation. As isapparent to one of ordinary skill in the art, plurality of monochromaticimages 420 can include any combination of the same and uniqueexcitations.

The excitation can be dependent upon a type of tissue expected. Forexample, for an expected tissue type of calcium and a CT imaging device,the excitation (whether as transmitted, as received, or both) can be 4keV. In another example, for an expected tissue type of oxygen and a CTimaging device, the excitation (whether as transmitted, as received, orboth) can be 530 eV.

In the example shown in FIG. 4, the plurality of monochromatic images420 are shown as taken with a CT imaging device and a excitation(whether as transmitted, as received, or both) at an exemplary range of65 KeV to 130 KeV, but can go as low, for example, 0.5 keV or as high as400 keV, depending on the tissues of interest. such spectra allowdiscrimination of tissue types identified as 440 among others.

The classification unit 430 can include a plurality of processes toanalyze the plurality of monochromatic images 420. The plurality ofprocesses can include a digital subtraction process, digital additionprocess, a multivariate statistical process, and/or an excitationselection process, as described in further detail below with respect toFIG. 5. The classification unit 430 can output data 440 indicative ofone or more classified tissue types (e.g., segmented image data fortissue types, and/or other data as shown above in FIG. 1).

The output data 440 can be classified tissue types of vascular leak,macrophages, angiogenesis, CALC map, LRNC map, FRESH IPH map, MATX map,Micro Calcification, erosion, OLD IPH map, thrombus, or any combinationthereof. As is apparent to one of ordinary skill in the art, the outputdata can be tissue types as are known in the art.

FIG. 5 is an example of a method 500 for improving soft tissuesegmentation, according to some embodiments of the invention. The methodcan involve obtaining a plurality of radiological images (e.g., CTimages, MRI images, ultrasound images). The radiological images can beobtained via a hybrid CT imaging device as described below in FIG. 6.Each of the plurality of radiological images can be obtained usingdifferent excitations (Step 510).

In some embodiments, the plurality of radiological image are CT imagesand the different excitations are different x-ray energy (whether astransmitted, as received, or both, and as a narrow energy range, or as abroader range). In some embodiments, the plurality of radiological imageare MR images and the different excitations are different radiofrequency pulses. In some embodiments, the plurality of radiologicalimage are ultrasound images and the different excitations are differentfrequencies.

The method can involve selecting a process among a plurality ofprocesses (e.g., via the classification unit 430) to analyze theplurality of radiological mages based on an expected soft tissue type(Step 520). The plurality of processes can include a digital subtractionprocess, digital addition process, a multivariate statistical process,and/or an excitation selection process.

The selection of the process among the plurality of processes can berandom, input by a user, and/or based on the characteristics of thetissue.

The digital subtraction process can involve taking one of the pluralityof radiological images image at one spectral range and subtracting itfrom another of the plurality of radiological images. In someembodiments, the digital subtraction can involve subtracting a firstsubset of the plurality of radiological images from one or more of theplurality of radiological images not in the subset. In some embodiments,the digital subtraction process can involve subtracting a narrow rangefrom a broad one, for example to cause a narrow band signal to be raisedabove a broad band noise floor. In some embodiments, the digitalsubtraction process can involve subtracting subtract one mediumbandwidth from another medium bandwidth, to distinguish two relatedtissue types.

The digital addition process can involve averaging the plurality ofradiological images. Averaging the plurality of radiological images canbe advantageous due to noise typically being similar across energies,whereas the tissue signal is not, such that averaging can result in ahigher signal to noise ratio.

The multivariate statistical process can involve combining each of theplurality of radiological images with a mathematical operator other thansimple subtraction or addition and removing any inter-class dependenciesthrough a multi-variate statistical approach from the set of techniquesidentified as non-linear operators.

The excitation selection process can involve selecting one or a subsetof radiological image of the plurality of images for a particular tissuetype, as certain tissue types can resolve better in one energy level ora subset of energy levels. For example, the energy dependence of atissue might be at a stable point at higher kVp levels or lower, and foranother tissue at another range, suggesting usage of the one range forone tissue, vs. the other tissue.

The processed plurality of radiological images can be input to one ormore classification models. In some embodiments, the classificationmodels may be trained as described in U.S. Pat. No. 11,094,058, filed onNov. 28, 2018, incorporated herein by reference in its entirety.

The method can also involve segmenting the processed plurality ofradiological images to display the soft tissue (Step 530). In someembodiments, segmenting processed plurality of excitations furthercomprises segmenting the medical image data into three-dimensional (3D)objects.

In some embodiments, segmenting the processed plurality of radiologicalimages includes segmenting the processed plurality of radiologicalimages into an outer wall boundary. In some embodiments, thesegmentation involves segmenting the plurality of radiological imagesinto a lumen and an outer wall based on a segmented lumen boundary,outer wall, perivascular region, and/or focal tissue boundaries. FIG. 4of U.S. Pat. No. 11,094,058, along with the corresponding description,shows an example of segmentation levels for a multi-scale vessel wallanalyte map.

FIG. 6 is a cross-sectional front view of a hybrid computed tomography(CT) scanner 600, processor 670, and two analog to digital converters655 to 665, according to some embodiments of the invention. The hybridCT scanner 600 can provide a truncated spectral scan and/or a localreconstruction constrained with global grayscale image from aconventional CT configuration.

The hybrid CT scanner 600 includes a first x-ray source 610, a secondx-ray source 620, a photon counting detector 630, an energy integratingdetector 640, a gantry 650. The energy integrating detector 640 can bepositioned relative to the first x-ray source 610 at predetermined anglebetween the photon counting detector 630 and the second x-ray source620. As shown in

FIG. 6, the predetermined angle is ˜90 degrees. The predetermined anglecan be based on optimizing a physical space around an arc to allow theintegrating detector to have wide coverage.

The first x-ray source 610 and/or the second x-ray source 620 can bepolychromatic x-ray sources. The first x-ray source 610 and/or thesecond x-ray source 620 can be any x-ray source as is known in the artto be used with CT scanners.

During operation, a patient 660 is inserted into the hybrid CT scanner600 and the first x-ray source 610 can direct a first x-ray attenuationtowards the patient 660 and the energy integrating detector 640. Theenergy integrated detector 640 can be dimensioned to produce an image ofa predetermined area of the patient 660.

The energy integrated detector 640 can have a curvature defined by aradius R and a diameter D and extend along an arc A, such that apredetermined area of the patient is imaged. For example, the radius Rcan be 30 inches, the diameter D can be 60 inches, and the arc A can be135 degrees.

In some embodiments, the first x-ray attenuation is transmitted at arange of 65 to 130 keV, or wider.

The energy integrating detector 640 receives at least a portion of thefirst x-ray attenuation and energy that has transmitted through thepatient 660.

The second x-ray source 620 can directs a second x-ray attenuationtowards the patient 660 and the photon counting detector 630. The photoncounting detector 630 can produce an image of a specific tissue targetwithin the predetermined area.

In some embodiments, the second x-ray attenuation is transmitted atrange similar than the first or different.

The photon counting detector 630 receives at least a portion of thesecond x-ray attenuation and energy that has transmitted through thepatient 660.

The energy integrating detector 640 and the photon counting detector 630communicate their respective received energy to the processor 670 viathe a/d converters 655, 665.

It has been known that the compressive sensing-based reconstructionalgorithms can be used to exactly reconstruct an region of interest ifan object is piecewise constant. TV-minimization-based spectral interiorreconstruction will find important applications in CT field, such asdose reduction, fast data acquisition, easy data storage and hardwarecost-reduction. To avoid the assumption made by many reconstructionschemes that assume a condition where the object imaged is piece-wiseconstant, which is not commonly satisfied in clinical CT imaging due totissue composition and/or contrast medium with gradient densityappreciably less than a voxel scale, we use a global energy-integratingimage to facilitate the interior spectral CT reconstruction and minimizethe reconstruction errors due to significant data truncations usingprocesser 670.

The processor 670 receives the outputs from the energy integratingdetector 640 and the photon counting detector 630 and processes theimages to produce a final CT image based on the image of thepredetermined area of the patient and the image of the specific tissuetarget within the predetermined image. The final CT image can be used asan input to the processes as described above in FIG. 5.

The processor 670 can include multiple processing modules. Theprocessing modules can include a module 671 to receive the spectral CTfrom the a/d converter 655 from the photon counting detector 630.Spectral CT can be transmitted to the photon counting detector signalprocessor module 672 to create sinograms. The sinograms are transmittedto the local scan module 673 to perform reconstruction. The module 675can receive the grayscale CT from the a/d converter 675 from the energyintegrating detector 640. The grayscale CT can be transmitted to theenergy integrating detector processor module 676 to create sinograms.The sinograms are transmitted to global scan module 677 to performreconstruction. The output (e.g., a narrowband output) of local scanmodule 673 and the output (e.g., multiple energy output that caninterrogate more energies than the local scan, (e.g., multiple narrowoutputs or a single wideband output) of the global scan module 677.

As is known in the art the processing modules on processor 670 can beimplemented in one processor or multiple processors.

The energy integrated detector 640 can be viewed as a global scan of thepatient 660 while the photon counting detector 630 can be viewed as alocal scan. The CT images from energy integrating detector 640 can beused for a standalone CT reconstruction or as used as global grayscaleconstraint for an interior spectral CT local tomography reconstructionusing photon counting detector 630. The photon counting detector 630 caninclude multiple energy bins that are configured to image a specifictissue target, multiple channels of reconstructed global grayscaleconstrained spectral images can be used to present and differentiatetissue.

An interior spectral CT reconstruction using grayscale image as a globalconstraint can be determined as follows:

Assume N is the number of spectrum channels (e.g., number of pluralityof energies or tube voltages of the CT images, I_(i) is a photonintensity of a specific spectrum channel, and i is channel index. Aprojection image of energy-integrating can be written as:

G=Σ_(i=1) ^(N) I _(i)exp(−∫μ_(i)(r)dr)   EQN. 1

where μ_(i)(r) for a specific spectral channel can be expressed as anattenuation decomposition from grayscale template, as follows:

μ_(i)(r)=μ_(gray)(r)+δμ_(i)(r)   EQN. 2

where μ_(gray)(r) Attenuation map reconstructed from energy-integratingprojection.

G=Σ _(i=1) ^(N) I _(i)exp(−∫[μ_(gray)(r)+δμ_(i)(r)]dr)   EQN. 3

i.e.

G=Σ_(i=1) ^(N) I _(i)exp(−∫μ_(gray)(r)dr)exp(−∫δμ_(i)(r)dr)   EQN. 4

Assume that δμ_(i)(r) is small enough using Tailor series expansion andignoring high-order terms, results in:

G=Σ _(i=1) ^(N) I _(i)exp(−∫μ_(gray)(r)dr)[1−∫δμ_(i)(r)dr]  EQN. 5

Assume C=exp (−∫μ_(gray)(r)dr) and substitute it in EQN. 5, results in:

G=ρ _(i=1) ^(N) I _(i) C[1−∫δμ_(i)(r)dr]  EQN. 6

It should be noted that

G=Σ_(i=1) ^(N)I_(i)C   EQN. 7

Suppose that there are three channels: N=3; The following equationsfollow:

I ₁ C∫δμ ₁(r)dr+I ₂ C∫δμ ₂(r)dr+I ₃ C∫δμ ₃(r)dr=I ₁ C+I ₂ C+I ₃ C−G=0  EQN. 8

B _(i) =I _(i)exp(−∫[μ_(gray)(r)+δμ_(i)(r)]dr)   EQN. 9

From B_(i)=I_(i)exp(−∫μ_(gray)(r)dr)exp(−∫δμ_(i)(r)dr), the results thelinear inverse equations as follows:

$\begin{matrix}{\left\{ \begin{matrix}{{{I_{1}C{\int{\delta{\mu_{1}(r)}{dr}}}} + {I_{2}C{\int{\delta{\mu_{2}(r)}{dr}}}} + {I_{3}C{\int{\delta{\mu_{3}(r)}dr}}}} = 0} \\{{\int{\delta{\mu_{1}(r)}dr}} = {- {\ln\left( \frac{B_{1}}{I_{1}C} \right)}}} \\{{\int{\delta{\mu_{2}(r)}dr}} = {- {\ln\left( \frac{B_{2}}{I_{2}C} \right)}}} \\{{\int{\delta{\mu_{3}(r)}dr}} = {- {\ln\left( \frac{B_{3}}{I_{3}C} \right)}}}\end{matrix} \right..} & {{EQNS}\mspace{14mu}\text{10-14}}\end{matrix}$

Where N is a total number of spectrum channels, I is Photon intensityemitted from x-ray source. I_(i) is Photon intensity at a spectralchannel, i: spectral channel index; μ is attenuation map to bereconstructed, B is Photon intensity detected from a spectral channel, Gis grayscale photon intensity detected, r is spatial position in a 3Dspace; δ is small variation and exp (x) is exponential function.

FIG. 7A shows an example of multi-scale association linking radiologyscale plaque morphology to molecular determinants, according to someembodiments of the invention. Prediction-based scoring, for example,including the validation of surrogate markers, may use (1) for dynamicvessel performance, examples including performance at hyperemia (e.g.,causative of ischemia) and/or rupture risk (e.g., causative ofinfarction) (2). Quantitation of morphology measurands, including forexample but not limited to IPH, include analysis of modalities, forexample including single-energy CTA, or multi-spectral CTA (3).Fluid-dynamics, e.g., shear stress assessment and finite element models(FEM) may elucidate mechanical triggers for smooth muscle celldifferentiation (SMC) differentiation (4), underpinning (2).

FIG. 7B is an example of multi-scale modeling, according to someembodiments of the invention. In FIG. 7B x represents a predispositionand/or expression (e.g., characterizing tissue) of cellular and/ormolecular level species, y represents macro-molecular tissuepresentation which may be assayed by radiology and/or used to classifyphenotype, and z represents predicted outcomes (e.g., stratifying risk),simulated progression, and/or simulated regression under differingclasses of therapy. If x and y are known, they may be used to trainmodels, generate hypotheses, and/or provide a base for simulation. If xis known but not y, or vice versa, prediction models can be utilized iftrained with examples where both x and y are known. One example purposecan be to provide means that may be practically and/or economicallyeasier to perform clinically to obtain information across scales.

In some embodiments, a causal relationships can be analyticallyestablished such as, does x cause y, and/or, if we see y, can themechanisms at the level of x plausibly create y be identified. In someembodiments, a utility for such analyses includes if knowing x changeswhat specific drug or surgical intervention can be optimal, thentreatment can be personalized.

In some embodiments, analytic methods can be used to establish whetherthe relationship between x and y may be spatially differentiated todetermine, for example, if the specificity may increase diagnosticconfidence by aboding dilution that occurs as a result of not accountingfor spatial context, analogous to the value of single cell techniquesrelative to techniques that do not provide differentiation.

FIG. 7C is an example of extended multi-scale modeling, according tosome embodiments of the invention. Extending the multi-scale modelingtechniques, x, y, and z can include adding time-dependent functions. Forexample, x(t) can capture plaque development, e.g., by premature aging,and/or other mechanistic explanations at cellular/molecular level, y(t)can capture macroscopic phenotyping observed at radiology over time, andz(t) can predict what happens next under candidate treatment plans(e.g., including if left untreated). In some embodiments, the analysesprovide y(t) as 3D objects validated at histology, for aspatially-resolved basis at the macroscopic level and/or also manifestin convolutional neural networks for phenotype classification. Toconnect to molecular/cellular level, literature may be mined, and tissueresources may be used to augment with de novo experiments for datacollection.

FIG. 8 is an example output screen 800 showing multiple simulatedevent-free survival possibilities under untreated and under varioustreatment scenarios, according to some embodiments of the invention. Thesimulated even-free survival possibilities include systemic inflationtherapeutics 810, intensive lipid lowering therapeutic 820, Statin s830, Revascularization (e.g., stent) 840, and no treatment 850.

Whether obtaining CT scans with single energy or multiple energy,complex tissue presentations (e.g., tissues in a region that includemore than one tissue type in certain locations) can be identified. Forexample, a complex tissue region can be microcalcification over MATX, orover LRNC, etc. Adaptive grid sizing and/or adaptive region growing canbe used to create nuanced representations of complex tissue types.Complicated biology can be represented as a screen overlay or a gridpoint overlay and can determine the progression of the complicatedbiology, not only the end results. For example, progression ofmicrocalcification can be determined, not only an end result of densemacrocalcification.

Such screens or grid point overlays typically do not replace focaltissue presentations but can augment them. Continuing with themicrocalcification example, the subjective parlance has used the term“spotty calcification,” but this parlance can fail to representintermediate signs of focal organization. In some embodiment,intermediate signs of focal organization of microcalcification can bevisually represented as a “grid overlay” that represents points, eithersparse or dense (e.g., not material density, but point density) to showa pattern of organization.

The grid overlay can be represented for the purpose of helping humanobservers to visualize in the software as a “texture” and/or can berepresented on data objects that are well suited to computer processingas a “mottling.” In this manner, the tissue organization can affect notonly MATX, but also LRNC and/or other tissue regions also. Like IPH,micro-calcification can be subdivided into two tissue/stage types: denseand/or early stage. Microcalcification can be a grid, whereas densemacrocalcification can be focal. The microcalcification grid can beinterpreted as a distribution of niduses which can generalize how tissueis represented capable of expressing transitional states where theniduses collect. For example, not all the way to dense yet but gettingcloser. Tissue types of vascular leak, microcalcification, angiogenesiscan be represented as grids of varying point densities. Tissue typesexpressed as focal regions like LRNC, CALC, and IPH can be focalregions. MATX can be expressed as a focal region, but with regions thatare better described as other tissues.

FIG. 9 shows a method 900 for determining and displaying mixed tissuetypes, according to some embodiments of the invention.

The method can involve receiving a radiological image of a patient (Step910). The radiological image can be a CT image, MR image or anultrasound image. For a radiological image of a CT image, the CT imagecan be obtained using the hybrid CT imaging device as described above inFIG. 5

The method can involve determining a first tissue type in a region ofinterest based on the radiological image of the patient, wherein thefirst tissue type is a grid of points in the region of interest (Step920). For example, the first tissue type can be any tissue type that canprogress from less to more dense. The first tissue type can be anytissue type that can be dispersed on another tissue type. The firsttissue type can be vascular leak, microcalcification and/orangiogenesis.

The grid of points can be each be represented as point densities. Eachpoint on the grid of points can have some of the same, all of the same,or unique density values.

The first tissue type can be determined via the system above in FIG. 4,the method shown above in FIG. 5, the system above in FIG. 6, or anycombination thereof.

The method can involve determining a second tissue type, in the regioninterest based on the radiological image of the patient, wherein thesecond tissue type is a focal region in the region of interest, whereinat least some of the grip point of the first tissue type coincide inposition with the second tissue type (Step 930). The second tissue typecan be any tissue type that can be expressed as a focal region. Thesecond tissue type can be LRNC, CALC, and/or IPH.

The second tissue type can be determined via the system above in FIG. 4,the method shown above in FIG. 5, the system above in FIG. 6, or anycombination thereof.

FIG. 10 shows a method 100 for determining and displaying mixed tissuetypes of a microcalcification screen and dense calcification regionsusing radiological images of multiple energy spectral CT images,according to some embodiments of the invention.

The method involves receiving multiple energy spectral CT images (e.g.,via the system of FIG. 4 as describe above) (Step 1010).

The method can involve performing multiple energy photon-counting K-edgesubtraction imaging (1020). In some embodiments, for K-edgedecomposition imaging for the multiple-energy system with the photoncounting detectors (PCDs), energy bins can significantly affect anintensity of the extracted K-edge signal to provide optimized energybins with the potential to improve classifications betweenmicro-calcifications and focal/dense calcifications.

The method can involve performing spectral image denoising withregularization models (1030). In some embodiments, linear attenuationcoefficient maps are decomposed into basis materials separable inspectral and space domains. Nonlinearities can be converted to thereconstruction of mass density maps. The dimensionality of theoptimization variables may be reduced and/or a minimization schemewhereby the reconstruction is solved with regularizations of weightednuclear norm and total variation, thus providing more spectralinformation with reduce noise.

The method can involve improving (e.g., cleaning) up the calcium (Step1040) by subtracting A from B to, for example, effectively improving thesignal to noise ratio.

The method can involve segmenting the improved calcium (Step 1050). Thesegmentation can result in a dense calcification focal region and amicrocalcification screen (e.g., grid overlay).

In some embodiments, principal component analysis (PCA) for featureextraction is used with spectral imaging to extract a small HUdifference of soft tissues presented in multiple energy images. Softtissues can be extracted by, for example, using adaptive region growingand K-means clustering technique. Region growing algorithms can be usedto separate regions that have the same properties that predefined aprior and can provide the original images of clear edges with goodsegmentation results. In some embodiments, stages of segmentation can beas follows: 1) a morphological reconstruction can be applied to smooththe flat area and/or preserve the edge of the image; 2) multiscalemorphological gradient can be used to avoid thickening and/or merging ofthe edges; 3) for contrast enhancement, a top/bottom hat transformationcan be used; 4) the morphological gradient of an image can be modifiedby imposing regional minima at the location of both the internal and theexternal markers; and 5) a weighted function can be used to combine thetop/bottom hat transformation algorithm and the markers algorithm to getthe new algorithm. In this manner, over segmentation from traditionalwatershed can be prevented.

FIG. 11 are diagrams showing examples of the method of FIG. 10,according to some embodiments of the invention. Diagram 1110 shows thereceived multiple energy spectral CT images (e.g., Step 1010 as shownabove in FIG. 10).

Diagram 1130 shows the output of performing multiple energyphoton-counting K-edge subtraction imaging in the CT images (e.g., Step1020 as shown above in FIG. 10). Diagram 1120 shows the output ofperforming spectral image denoising with regularization models on the CTimages (e.g., Step 1030 as shown above in FIG. 10). Diagram 1140 showsthe output of cleaning up the calcium (e.g., Step 1040 as shown above inFIG. 10). Diagrams 1150 and 1160 shows segmenting the cleaned calcium(e.g., Step 1050 as shown above in FIG. 10) into dense calcification1160 and microcalcification 1150.

Although embodiments of the invention are not limited in this regard,discussions utilizing terms such as, for example, “processing,”“computing,” “calculating,” “determining,” “establishing”, “analyzing”,“checking”, or the like, can refer to operation(s) and/or process(es) ofa computer, a computing platform, a computing system, or otherelectronic computing device, that manipulates and/or transforms datarepresented as physical (e.g., electronic) quantities within thecomputer's registers and/or memories into other data similarlyrepresented as physical quantities within the computer's registersand/or memories or other information non-transitory storage medium thatcan store instructions to perform operations and/or processes.

Although embodiments of the invention are not limited in this regard,the terms “plurality” and “a plurality” as used herein can include, forexample, “multiple” or “two or more”. The terms “plurality” or “aplurality” can be used throughout the specification to describe two ormore components, devices, elements, units, parameters, or the like. Theterm set when used herein can include one or more items. Unlessexplicitly stated, the method embodiments described herein are notconstrained to a particular order or sequence. Additionally, some of thedescribed method embodiments or elements thereof can occur or beperformed simultaneously, at the same point in time, or concurrently.

A computer program can be written in any form of programming language,including compiled and/or interpreted languages, and the computerprogram can be deployed in any form, including as a stand-alone programor as a subroutine, element, and/or other unit suitable for use in acomputing environment. A computer program can be deployed to be executedon one computer or on multiple computers at one site.

Method steps can be performed by one or more programmable processorsexecuting a computer program to perform functions of the invention byoperating on input data and generating output. Method steps can also beperformed by an apparatus and can be implemented as special purposelogic circuitry. The circuitry can, for example, be a FPGA (fieldprogrammable gate array) and/or an ASIC (application-specific integratedcircuit). Modules, subroutines, and software agents can refer toportions of the computer program, the processor, the special circuitry,software, and/or hardware that implement that functionality.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read-only memory or arandom access memory or both. The essential elements of a computer are aprocessor for executing instructions and one or more memory devices forstoring instructions and data. Generally, a computer can be operativelycoupled to receive data from and/or transfer data to one or more massstorage devices for storing data (e.g., magnetic, magneto-optical disks,or optical disks).

Data transmission and instructions can also occur over a communicationsnetwork. Information carriers suitable for embodying computer programinstructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices. Theinformation carriers can, for example, be EPROM, EEPROM, flash memorydevices, magnetic disks, internal hard disks, removable disks,magneto-optical disks, CD-ROM, and/or DVD-ROM disks. The processor andthe memory can be supplemented by, and/or incorporated in specialpurpose logic circuitry.

To provide for interaction with a user, the above-described techniquescan be implemented on a computer having a display device, a transmittingdevice, and/or a computing device. The display device can be, forexample, a cathode ray tube (CRT) and/or a liquid crystal display (LCD)monitor. The interaction with a user can be, for example, a display ofinformation to the user and a keyboard and a pointing device (e.g., amouse or a trackball) by which the user can provide input to thecomputer (e.g., interact with a user interface element). Other kinds ofdevices can be used to provide for interaction with a user. Otherdevices can be, for example, feedback provided to the user in any formof sensory feedback (e.g., visual feedback, auditory feedback, ortactile feedback). Input from the user can be, for example, received inany form, including acoustic, speech, and/or tactile input.

The computing device can include, for example, a computer, a computerwith a browser device, a telephone, an IP phone, a mobile device (e.g.,cellular phone, personal digital assistant (PDA) device, laptopcomputer, electronic mail device), and/or other communication devices.The computing device can be, for example, one or more computer servers.The computer servers can be, for example, part of a server farm. Thebrowser device includes, for example, a computer (e.g., desktopcomputer, laptop computer, and tablet) with a World Wide Web browser(e.g., Microsoft® Internet Explorer® available from MicrosoftCorporation, Chrome available from Google, Mozilla® Firefox availablefrom Mozilla Corporation, Safari available from Apple). The mobilecomputing device includes, for example, a personal digital assistant(PDA).

Website and/or web pages can be provided, for example, through a network(e.g., Internet) using a web server. The web server can be, for example,a computer with a server module (e.g., Microsoft® Internet InformationServices available from Microsoft Corporation, Apache Web Serveravailable from Apache Software Foundation, Apache Tomcat Web Serveravailable from Apache Software Foundation).

The storage module can be, for example, a random access memory (RAM)module, a read only memory (ROM) module, a computer hard drive, a memorycard (e.g., universal serial bus (USB) flash drive, a secure digital(SD) flash card), a floppy disk, and/or any other data storage device.Information stored on a storage module can be maintained, for example,in a database (e.g., relational database system, flat database system)and/or any other logical information storage mechanism.

The above-described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributing computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The components ofthe system can be interconnected by any form or medium of digital datacommunication (e.g., a communication network). Examples of communicationnetworks include a local area network (LAN), a wide area network (WAN),the Internet, wired networks, and/or wireless networks.

The system can include clients and servers. A client and a server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

The above described networks can be implemented in a packet-basednetwork, a circuit-based network, and/or a combination of a packet-basednetwork and a circuit-based network. Packet-based networks can include,for example, the Internet, a carrier internet protocol (IP) network(e.g., local area network (LAN), wide area network (WAN), campus areanetwork (CAN), metropolitan area network (MAN), home area network (HAN),a private IP network, an IP private branch exchange (IPBX), a wirelessnetwork (e.g., radio access network (RAN), 802.11 network, 802.16network, general packet radio service (GPRS) network, HiperLAN), and/orother packet-based networks. Circuit-based networks can include, forexample, the public switched telephone network (PSTN), a private branchexchange (PBX), a wireless network (e.g., RAN, Bluetooth®, code-divisionmultiple access (CDMA) network, time division multiple access (TDMA)network, global system for mobile communications (GSM) network), and/orother circuit-based networks.

Some embodiments of the present invention may be embodied in the form ofa system, a method or a computer program product. Similarly, someembodiments may be embodied as hardware, software or a combination ofboth. Some embodiments may be embodied as a computer program productsaved on one or more non-transitory computer readable medium (or media)in the form of computer readable program code embodied thereon. Suchnon-transitory computer readable medium may include instructions thatwhen executed cause a processor to execute method steps in accordancewith embodiments. In some embodiments the instructions stores on thecomputer readable medium may be in the form of an installed applicationand in the form of an installation package.

Such instructions may be, for example, loaded by one or more processorsand get executed. For example, the computer readable medium may be anon-transitory computer readable storage medium. A non-transitorycomputer readable storage medium may be, for example, an electronic,optical, magnetic, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any combination thereof.

Computer program code may be written in any suitable programminglanguage. The program code may execute on a single computer system, oron a plurality of computer systems.

One skilled in the art will realize the invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of theinvention described herein. Scope of the invention is thus indicated bythe appended claims, rather than by the foregoing description, and allchanges that come within the meaning and range of equivalency of theclaims are therefore intended to be embraced therein.

In the foregoing detailed description, numerous specific details are setforth in order to provide an understanding of the invention. However, itwill be understood by those skilled in the art that the invention can bepracticed without these specific details. In other instances, well-knownmethods, procedures, and components, modules, units and/or circuits havenot been described in detail so as not to obscure the invention. Somefeatures or elements described with respect to one embodiment can becombined with features or elements described with respect to otherembodiments.

1. A computerized method for improving soft tissue analysis, the methodcomprising: obtaining, via a computing device, a plurality ofradiological images of patient, where each of the radiological images isobtained using different excitations; selecting, by the computingdevice, a process among a plurality of processes to analyze theplurality of excitations based on an expected soft tissue type; andsegmenting, by the computer devices, the processed plurality ofexcitations to display the soft tissue.
 2. The computerized method ofclaim 1 wherein the plurality of radiological image are computerizedtomography (CT) images and the different excitations are different x-rayenergy.
 3. The computerized method of claim 1 wherein the plurality ofradiological image are Magnetic Resonance (MR) images and the differentexcitations are different radio frequency pulses.
 4. The computerizedmethod of claim 1 wherein the plurality of radiological image areultrasound images and the different excitations are differentfrequencies.
 5. The computerized method of claim 1 further comprising:determining, via, the computing device, a first tissue type in a regionof interest based on the plurality of radiological images of thepatient, wherein the first tissue type is a represented by a grid ofpoints across the region of interest; and determining, via the computingdevice, a second tissue type, in the region interest based on theplurality of radiological images of the patient, wherein the secondtissue type is a focal region in the region of interest, wherein atleast some of the grid points of the first tissue type coincide inposition with the second tissue type.
 6. The computerized method ofclaim 1 wherein the plurality of processes comprises a digitalsubtraction process, digital addition process, a multivariatestatistical process, or an excitation selection process.
 7. Thecomputerized method of claim 6 wherein the digital subtraction processcomprises subtracting a first subset of the plurality of radiologicalimages from one or more of the plurality of radiological images not inthe subset.
 8. The computerized method of claim 6 wherein the digitaladdition process comprises averaging the received plurality ofradiological images.
 9. The computerized method of claim 6 wherein themultivariate statistical process comprises: combining the plurality ofradiological images; and removing inter-class dependencies through amulti-variate statistical approach.
 10. The computerized method of claim1 wherein the plurality of radiological images are CT images and each ofthe plurality of CT images are formed by: directing, via a first x-raysource, a first x-ray attenuation to an energy integrating detector,wherein the energy integrated detector is dimensioned to produce animage of a predetermined area of the patient; directing, via a secondx-ray source, a second x-ray attenuation to a photon counting detector,where the photon counting detector to produce an image of a specifictissue target within the predetermined area; and producing, via aprocessor, a final CT image based on the image of the predetermined areaof the patient and the image of the specific tissue target within thepredetermined image.
 11. The computerized method of claim 6 wherein theexcitation selection process involves selecting a particularradiological image of the plurality of radiological images based on thetissue type.
 12. A hybrid computerized tomography (CT) scannercomprising: a first x-ray source that directs a first x-ray attenuationto an energy integrating detector, wherein the energy integrateddetector is dimensioned to produce an image of a predetermined area of apatient; a second x-ray source that directs a second x-ray attenuationto a photon counting detector, where the photon counting detector toproduce an image of a specific tissue target within the predeterminedarea; and a processor to produce a final CT image based on the image ofthe predetermined area of the patient and the image of the specifictissue target within the predetermined image.
 13. The hybrid scanner ofclaim 12 wherein the energy integrating detector and the photon countingdetector are positioned to interrogate the same field of view.
 14. Thehybrid CT scanner of claim 12 wherein the image of a predetermine areaof a patient is a grayscale CT image
 15. The hybrid CT scanner of claim12 wherein the image of the specific tissue target within thepredetermined image is a spectral CT image.
 16. The hybrid CT scanner ofclaim 12 wherein the energy integrating detector relative to its firstx-ray source is positioned at a 90 degree difference between the photoncounting detector and its second x-ray source.
 17. The hybrid CT scannerof claim 12 wherein the processor is configured to analyze tissue types,the analysis comprising: selecting a process among a plurality ofprocesses to analyze the final CT image based on an expected soft tissuetype; and segmenting the processed final CT image to display the softtissue.
 18. The hybrid CT scanner of claim 12 wherein the photoncounting detector comprises multiple energy bins that are configured toimage a specific tissue target.
 19. A computerized method fordetermining and displaying mixed tissue types, the method comprising:receiving, via a computing device, a radiological image of a patient;determining, via the computing device, a first tissue type in a regionof interest based on the radiological image of the patient, wherein thefirst tissue type is a represented by a grid of points across the regionof interest; and determining, via the computing device, a second tissuetype, in the region interest based on the radiological image of thepatient, wherein the second tissue type is a focal region in the regionof interest, wherein at least some of the grid points of the firsttissue type coincide in position with the second tissue type.
 20. Thecomputerized method of claim 19 wherein the plurality of radiologicalimage are computerized tomography (CT) images, Magnetic Resonance (MR)images, or ultrasound images.
 21. The computerized method of claim 19wherein the first tissue type and the second tissue type are overlaidwhen displayed.
 22. The computerized method of claim 19 wherein the gridof points has varying densities.
 23. The computerized method of claim 19wherein the first tissue type is micro calcification, and the secondtissue type is LNRC, dense calcification, or IPH.
 24. The computerizedmethod of claim 19 further comprising: performing, via the computingdevice, multiple energy photon-counting K-edge subtraction imaging onthe CT images; performing, via the computing device, spectral imagedenoising with regularization models on the CT images; improving thesignal to noise ratio, via the computing device, of the calcium on thek-edge subtracted and de-noised CT images; and segmenting, via thecomputing device, the improved calcium images to represent one or bothof focally dense calcification and distributed microcalcification.